The present embodiments relate to prognosis modeling. Prognosis modeling attempts to predict the outcome, such as survivability, reoccurrence, effectiveness, and/or side effects, based on a proposed treatment or course of action. The prognosis modeling is performed for any desired disease, such as cancer.
There has been a steady increase of types of treatment for cancer, and thus treatment decision-making requires an assessment of risks and benefits associated with a specific combination of patient and treatment characteristics. Statistical models have proven useful for predicting prognosis and treatment outcome. These models are derived from the data collected at an institution. The data from other patients is used to create the model. The other patients are associated with different treatments and/or outcomes. The statistical model is formed by analyzing patient characteristics for these patients. However, data driven models typically require a large database of medical records representing previous treatment of many patients for statistical accuracy. Such databases may not be conveniently available or formatted for modeling. Using databases with records for a fewer number of medical records may provide less accuracy.